Field of the Invention
Embodiments disclosed herein relate to computer software. More specifically, embodiments disclosed herein relate to computer software that uses visual salience of a video as a predictor of success of the video.
Description of the Related Art
Producers of video content need analytics that can predict the success of a video before it has been released. Traditionally, predictions for feature films have been based on aspects surrounding the video, such as the video's genre, budget, the popularity of starring actors, critical reviews, and the like. Recently, social media content and other Internet sources have been leveraged to predict success. Many of these factors are subjective measures that potentially bias the results. For example, when relying on box office sales as a success measure, the results strongly depend on the chosen time window (such as first weeks, cumulative period in a theater, theater and video sales, etc.). Further still, available prediction models focus on full-length feature films, and are therefore of limited value when predicting the success of shorter videos such as commercials, trailers, and other content that is becoming prevalent on various streaming websites. However, computational measures of video assets themselves can serve as useful predictors. Specifically, computational models of human visual attention have not been applied to predict video success.